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A Hybrid Collision Model for Safety Collision Control
Published in IEEE International Conference on Robotics and Automation (ICRA), 2021
This paper describes the combination of geometric primitives and data-driven approaches to safely avoid the self-collisions on a quadruped.
Recommended citation: T. Noël, T. Flayols, J. Mirabel, J. Carpentier and N. Mansard, "A Hybrid Collision Model for Safety Collision Control," 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 1722-1728, doi: 10.1109/ICRA48506.2021.9561730.
Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
To improve safety and sparsity of navigation roadmaps, we propose a sampling-driven approach treating the roadmap nodes as free-space bubbles.
Recommended citation: T. Noël, S. Kabbour, A. Lehuger, E. Marchand and F. Chaumette, "Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment", 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 5707-5714, doi: 10.1109/IROS47612.2022.9981136.
Skeleton Disk-Graph Roadmap: A Sparse Deterministic Roadmap For Safe 2D Navigation and Exploration
Published in IEEE Robotics and Automation Letters (RA-L), 2023
Relying on the skeleton of the Signed-Distance Field (SDF) of the environment, we propose a deterministic roadmap construction method suitable for navigation and exploration.
Recommended citation: T. Noël, A. Lehuger, E. Marchand and F. Chaumette, "Skeleton Disk-Graph Roadmap: A Sparse Deterministic Roadmap for Safe 2D Navigation and Exploration," in IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 555-562, Jan. 2024, doi: 10.1109/LRA.2023.3334103.
(PhD manuscript) Autonomous Exploration of an Unknown 3D Environment
Published in HAL open-science archive, 2025
Abstract: This manuscript investigates the problem of robotic autonomous exploration in 3D environments. It is a transversal research question involving various robotics tasks such as mapping, motion planning, and control. From a high-level standpoint, exploration can be seen as the maximization of the information gain about the environment, often combined with a minimization of energy expenditure. Considering the standard action-perception loop, exploration presents a strong coupling between past sensor observations and future actions, as the progressive discovery of the environment dynamically dictates the future optimal sensor path. This coupling is even more pronounced for directional sensors, e.g. cameras, for which the robot state drastically impacts the sensors observations. Deriving high-quality plans from the current environment state is thus essential, but remains challenging due to the high uncertainties in the workspace.
Recommended citation: T. Noël, "Autonomous exploration of an unknown 3D environment". Robotics [cs.RO]. Université de Rennes, 2025. English. ⟨NNT : 2025URENS005⟩. ⟨tel-05127636⟩